Computer Sciences Commons^™

Contributions To The Solution Of Large Nonlinear Systems Via Model-Order Reduction And Interval Constraint Solving Techniques, Leobardo Valera

Open Access Theses & Dissertations

Many engineering problems boil down to solving partial differential equations (PDEs) that describe real-life phenomena. Nevertheless, efficiently and reliably solving such problems constitutes a major challenge in computational sciences and in engineering in general.

PDE-based systems can reach sizes so large after they are discretized. The large size in these problems generate several issues, among them we can mention: large space of storing, computing time, and the most important, lost of accuracy. A popular approach to solving such problems is assume that the PDE's solution is in a subspace, and the solution is sought there. This assumption and later searching …

Superior Decoupled Control Of Active And Reactive Power For Three-Phase Voltage Source Converters, Hesam Rahbarimagham, Erfan Maali Amiri, Behrooz Vahidi, Gevorg Babamalek Gharehpetian, Mehrdad Abedi

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents an active-reactive power control strategy for voltage source converters (VSCs) based on derivation of the direct and quadrature components of the VSC output current. The proposed method utilizes a multivariable proportional-integral controller and provides almost completely decoupled control capability of the active and reactive power with almost full disturbance rejection due to step changes in the power exchanged between the VSC and the grid. It also imposes fast transient response and zero steady-state error as compared to the conventional power control approaches. The applicability of the proposed power control strategy for providing the robust stability of the …

Determination Of Electromagnetic Properties Of Steel For Prediction Of Stray Losses In Power Transformers, Leonardo Strac, Damir Zarko

Turkish Journal of Electrical Engineering and Computer Sciences

This paper introduces a method for determination of equivalent linear electromagnetic parameters (constant complex permeability and electrical conductivity) of nonlinear magnetic steel, which can be used in a time-harmonic finite-element simulation to yield the same losses in the volume of that material as the measured ones. The conductivity and the static hysteresis loop of the steel have been measured, from which complex permeability as a function of flux density has been extracted. The indirect measurement of losses in various samples of nonmagnetic and magnetic steel has been carried out using a physical model of a transformer core with a coil. …

Concept Of Online Assisted Platform For Technologies And Management In Communications – Optimek, Galia Marinova, Vassil Guliashki, Ognyan Chikov

UBT International Conference

The paper describes the concept of a Multimodular Multydisciplinary platform, contacting through unified templates in a Portal with knowledge, with Useful INTERNET resources, in order to provide advanced research and education. Usually the online resources available are mainly in the area of e- and distance education, but still an understanding is missing for the scale and the use of studying and the systematization of the online resource. The new concept has an accent of the useful INTERNET resource and the development of a System of nets to it, in the aim of solving tasks and generating new knowledge in the …

Scalable Tuning Of Building Models To Hourly Data, Aaron Garrett, Joshua New

Research, Publications & Creative Work

Energy models of existing buildings are unreliable unless calibrated so that they correlate well with actual energy usage. Manual tuning requires a skilled professional and is prohibitively expensive for small projects, imperfect, nonrepeatable, and not scalable to the dozens of sensor channels that smart meters, smart appliances, and sensors are making available. A scalable, automated methodology is needed to quickly, intelligently calibrate building energy models to all available data, increase the usefulness of those models, and facilitate speed-and-scale penetration of simulation-based capabilities into the marketplace for actualized energy savings. The "Autotune" project is a novel, model-agnostic methodology that leverages supercomputing, …

Optimization Of Switch Virtual Keyboard By Using Computational Modelling, Xiao Zhang

Open Access Theses

In this thesis, I first reviewed some keyboard technologies used by people with motor difficulties, and described design elements that influence efficiency. I cast the design of a switch keyboard as an optimization problem, and arrangement of keys on such a keyboard as a Mixed Integer Programming problem. One significant variable in the MIP problem, the error rate, is related to several other variables. I treated modeling of the error rate as a parameter estimation problem, and used a data mining method. I designed HCI experiments to gather data for parameter estimation, using Bayesian logistic regression model. The empirical data …

Reducing Carbon Emission Of Ocean Shipments By Optimizing Container Size Selection, Edwin Lik Ming Chong, Nang Laik Ma, Kar Way Tan

Research Collection School Of Computing and Information Systems

Human’s impact on earth through global warming is more or less an accepted fact. Ocean freight is estimated to contribute 4-5% of global carbon emissions and manufacturing companies can aid in reducing this amount. Many companies that ship goods through full container loads do not have the capabilities to ensure the containers they are using minimizes their carbon footprint. One of the reasons is the choice of non-ideal container sizes for their shipments. This paper provides a mathematical model to minimize companies’ shipping carbon footprints by selecting the ideal container sizes appropriate for their shipment volumes. Using data from a …

Optimal Acceleration Thresholds For Non-Holonomic Agents, Brian Ricks, Parris K. Egbert

Computer Science Faculty Publications

Finding optimal trajectories for non-accelerating, non-holonomic agents is a well-understood problem. However, in video games, robotics, and crowd simulations non-holonomic agents start and stop frequently. With the vision of improving crowd simulation, we find optimal paths for virtual agents accelerating from a standstill. These paths are designed for the “ideal”, initial stage of planning when obstacles are ignored. We analytically derive paths and arrival times using arbitrary acceleration angle thresholds. We use these paths and arrival times to find an agent’s optimal ideal path. We then numerically calculate the decision surface that can be used by an application at run-time …

Scheduling And Resource Allocation In Wireless Sensor Networks, Yosef Alayev

Dissertations, Theses, and Capstone Projects

In computer science and telecommunications, wireless sensor networks are an active research area. Each sensor in a wireless sensor network has some pre-defined or on demand tasks such as collecting or disseminating data. Network resources, such as broadcast channels, number of sensors, power, battery life, etc., are limited. Hence, a schedule is required to optimally allocate network resources so as to maximize some profit or minimize some cost. This thesis focuses on scheduling problems in the wireless sensor networks environment. In particular, we study three scheduling problems in the wireless sensor networks: broadcast scheduling, sensor scheduling for area monitoring, and …

R Code To Accompany “Principal Component Analysis And Optimization: A Tutorial”, Robert Reris, J. Paul Brooks

Statistical Sciences and Operations Research Data

This data accompanies "Principal Component Analysis and Optimization: A Tutorial" by Robert Reris and J. Paul Brooks, presented at the 2015 INFORMS Computing Society Conference, Operations Research and Computing: Algorithms and Software for Analytics, Richmond, Virginia January 11-13, 2015.

The data contains R code, output, and comments that follow the examples for principal component analysis in the paper.

A Distributed Consensus Algorithm For Decision Making In Service-Oriented Internet Of Things, Shancang Li, George Oikonomou, Theo Tryfonas, Thomas M. Chen, Li Da Xu

Information Technology & Decision Sciences Faculty Publications

In a service-oriented Internet of things (IoT) deployment, it is difficult to make consensus decisions for services at different IoT edge nodes where available information might be insufficient or overloaded. Existing statistical methods attempt to resolve the inconsistency, which requires adequate information to make decisions. Distributed consensus decision making (CDM) methods can provide an efficient and reliable means of synthesizing information by using a wider range of information than existing statistical methods. In this paper, we first discuss service composition for the IoT by minimizing the multi-parameter dependent matching value. Subsequently, a cluster-based distributed algorithm is proposed, whereby consensuses are …

Performance Modeling And Optimization Techniques For Heterogeneous Computing, Supada Laosooksathit

Doctoral Dissertations

Since Graphics Processing Units (CPUs) have increasingly gained popularity amoung non-graphic and computational applications, known as General-Purpose computation on GPU (GPGPU), CPUs have been deployed in many clusters, including the world's fastest supercomputer. However, to make the most efficiency from a GPU system, one should consider both performance and reliability of the system.

This dissertation makes four major contributions. First, the two-level checkpoint/restart protocol that aims to reduce the checkpoint and recovery costs with a latency hiding strategy in a system between a CPU (Central Processing Unit) and a GPU is proposed. The experimental results and analysis reveals some benefits, …

Extremum-Seeking For Nonlinear Discrete-Time Systems With Application To Hcci Engines, H. Zargarzadeh, S. Jagannathan, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

For many control applications, identifying an optimal operating point by maximizing/minimizing a performance function is important. This paper applies the extremum-seeking method to nonaffine, nonlinear discrete-time systems stabilized by an optimal adaptive controller. First, a novel averaging method is used for the nonlinear discrete-time systems to show that their output unique extrema are stable equilibrium points. Then, a singular perturbation method in discrete time is employed to show that the overall closed loop system will dynamically converge to the extremum. The applicability of this scheme is numerically verified on a Homogeneous Charge Compression Ignition (HCCI) model validated experimentally and expressed …

A Reduced Probabilistic Neural Network For The Classification Of Large Databases, Abdelhadi Lotfi, Abdelkader Benyettou

Turkish Journal of Electrical Engineering and Computer Sciences

The probabilistic neural network (PNN) is a special type of radial basis neural network used mainly for classification problems. Due to the size of the network after training, this type of network is usually used for problems with a small-sized training dataset. In this paper, a new training algorithm is presented for use with large training databases. Application to the handwritten digit database shows that the reduced PNN performs better than the standard PNN for all of the studied cases with a big gain in size and processing speed. This new type of neural network can be used easily for …

Contributions To Global Optimization Using Interval Methods And Speculation, Angel Fernando Garcia Contreras

Open Access Theses & Dissertations

Most electronic devices we are familiar with, such as cell phones and computers, are small and require similarly small electronic components arranged and connected in small areas. Finding the right size and arrangement of the components inside a device can be a challenge. The manufacturing process of the components limits their possible size, some components have specific needs to operate at a certain speed, and the total area of the device is also limited. In portable devices, these designs have one important objective: that the entire device consumes the minimum amount of electricity possible, so the device can keep functioning …

Simultaneous Optimization Of The Cavity Heat Load And Trip Rates In Linacs Using A Genetic Algorithm, Balša Terzić, Alicia S. Hofler, Cody J. Reeves, Sabbir A. Khan, Geoffrey A. Krafft, Jay Benesch, Arne Freyberger, Desh Ranjan

Physics Faculty Publications

In this paper, a genetic algorithm-based optimization is used to simultaneously minimize two competing objectives guiding the operation of the Jefferson Lab's Continuous Electron Beam Accelerator Facility linacs: cavity heat load and radio frequency cavity trip rates. The results represent a significant improvement to the standard linac energy management tool and thereby could lead to a more efficient Continuous Electron Beam Accelerator Facility configuration. This study also serves as a proof of principle of how a genetic algorithm can be used for optimizing other linac-based machines.

Online Portfolio Selection: A Survey, Bin Li, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Online portfolio selection is a fundamental problem in computational finance, which has been extensively studied across several research communities, including finance, statistics, artificial intelligence, machine learning, and data mining. This article aims to provide a comprehensive survey and a structural understanding of online portfolio selection techniques published in the literature. From an online machine learning perspective, we first formulate online portfolio selection as a sequential decision problem, and then we survey a variety of state-of-the-art approaches, which are grouped into several major categories, including benchmarks, Follow-the-Winner approaches, Follow-the-Loser approaches, Pattern-Matching--based approaches, and Meta-Learning Algorithms. In addition to the problem formulation …

Optimization Of Job Shop Scheduling Problems Using Modified Clonal Selection Algorithm, Yilmaz Atay, Hali̇fe Kodaz

Turkish Journal of Electrical Engineering and Computer Sciences

Artificial immune systems (AISs) are one of the artificial intelligence techniques studied a lot in recent years. AISs are based on the principles and mechanisms of the natural immune system. In this study, the clonal selection algorithm, which is used commonly in AISs, is modified. This algorithm is applied to job shop scheduling problems, which are one of the most difficult optimization problems. For applying application results to the optimum solution, parameter values giving the optimum solution are determined by analyzing the parameters in the algorithm. The obtained results are given in detail in the tables and figures. The best …

A Scalable Backward Chaining-Based Reasoner For A Semantic Web, Hui Shi, Kurt Maly, Steven Zeil

Computer Science Faculty Publications

In this paper we consider knowledge bases that organize information using ontologies. Specifically, we investigate reasoning over a semantic web where the underlying knowledge base covers linked data about science research that are being harvested from the Web and are supplemented and edited by community members. In the semantic web over which we want to reason, frequent changes occur in the underlying knowledge base, and less frequent changes occur in the underlying ontology or the rule set that governs the reasoning. Interposing a backward chaining reasoner between a knowledge base and a query manager yields an architecture that can support …

Swarm Intelligence As An Optimization Technique, Alma Bregaj

UBT International Conference

Optimization techniques inspired by swarm intelligence have become increasingly popular during the last years. Swarm intelligence is based on nature-inspired behaviours and is successfully applied to optimisation problems in a variety of fields. The advantage of these approaches over traditional techniques is their robustness and flexibility. These properties make swarm intelligence a successful design paradigm for algorithms that deal with increasingly complex problems. In this paper I am focused on the comparison between different swarm-based optimisation algorithms and I have presented some examples of real practical applications of these algorithms.

Arrival Time Based Traffic Signal Optimization For Intelligent Transportation Systems, Vamsi Paruchuri, Sriram Chellappan, Rathinasamy B. Lenin

Computer Science Faculty Research & Creative Works

Road Transportation is a crucial component of today's society, which drives several facets of our lives. The goal of intelligent transportation systems (ITS) is to improve the effectiveness, efficiency, and safety of the transportation system. Traffic signals are an elementary component of all road transportation systems. In order to maximize the productivity of a city, traffic signals must be able to efficiently control the flow of vehicles. Traditionally, current traffic signal optimization is based on traffic arrival rates, either estimated or forecasted. In this paper, we illustrate that arrival time-based solutions can outperform arrival rate-based approaches. To the best of …

√(X2 + Μ) Is The Most Computationally Efficient Smooth Approximation To |X|: A Proof, Carlos Ramirez, Reinaldo Sanchez, Vladik Kreinovich, Miguel Argaez

Departmental Technical Reports (CS)

In many practical situations, we need to minimize an expression of the type |c₁| + ... + |c_n|. The problem is that most efficient optimization techniques use the derivative of the objective function, but the function |x| is not differentiable at 0. To make optimization efficient, it is therefore reasonable to approximate |x| by a smooth function. We show that in some reasonable sense, the most computationally efficient smooth approximation to |x| is the function √(x² + μ), a function which has indeed been successfully used in such optimization.

Master Physician Scheduling Problem, Aldy Gunawan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We study a real-world problem arising from the operations of a hospital service provider, which we term the master physician scheduling problem. It is a planning problem of assigning physicians’ full range of day-to-day duties (including surgery, clinics, scopes, calls, administration) to the defined time slots/shifts over a time horizon, incorporating a large number of constraints and complex physician preferences. The goals are to satisfy as many physicians’ preferences and duty requirements as possible while ensuring optimum usage of available resources. We propose mathematical programming models that represent different variants of this problem. The models were tested on a real …

Artificial Immune Systems And Particle Swarm Optimization For Solutions To The General Adversarial Agents Problem, Jeremy Mange

Dissertations

The general adversarial agents problem is an abstract problem description touching on the fields of Artificial Intelligence, machine learning, decision theory, and game theory. The goal of the problem is, given one or more mobile agents, each identified as either “friendly" or “enemy", along with a specified environment state, to choose an action or series of actions from all possible valid choices for the next “timestep" or series thereof, in order to lead toward a specified outcome or set of outcomes. This dissertation explores approaches to this problem utilizing Artificial Immune Systems, Particle Swarm Optimization, and hybrid approaches, along with …

Optimized Simulation Of Granular Materials, Seth R. Holladay

Theses and Dissertations

Visual effects for film and animation often require simulated granular materials, such as sand, wheat, or dirt, to meet a director's needs. Simulating granular materials can be time consuming, in both computation and labor, as these particulate materials have complex behavior and an enormous amount of small-scale detail. Furthermore, a single cubic meter of granular material, where each grain is a cubic millimeter, would contain a billion granules, and simulating all such interacting granules would take an impractical amount of time for productions. This calls for a simplified model for granular materials that retains high surface detail and granular behavior …

Fair Cost Sharing Auction Mechanisms In Last Mile Ridesharing, Duc Thien Nguyen

Dissertations and Theses Collection (Open Access)

With rapid growth of transportation demands in urban cities, one major challenge is to provide efficient and effective door-to-door service to passengers using the public transportation system. This is commonly known as the Last Mile problem. In this thesis, we consider a dynamic and demand responsive mechanism for Ridesharing on a non-dedicated commercial fleet (such as taxis). This problem is addressed as two sub-problems, the first of which is a special type of vehicle routing problems (VRP). The second sub-problem, which is more challenging, is to allocate the cost (i.e. total fare) fairly among passengers. We propose auction mechanisms where …

Making Solution Pluralism In Policy Making Accessible: Optimization Of Design And Services For Constituent Well-Being, Margeret A. Hall, Steven O. Kimbrough, Wibke Michalk, Jefff Schneider, Christof Weinhardt

Interdisciplinary Informatics Faculty Proceedings & Presentations

Policy makers are increasingly turning to computational support mechanisms for managing uncertainty, and constituent focused-decisions. Utilization and standardization of human-computer interaction principles to create solution pluralism (the condition of having a consideration set containing a multiplicity of credible solutions) is a fundamental to fulfilling this need. There is a need for standardized applications and user interfaces to deliver a higher quality of service, which assists policy makers in maintaining or increasing constituent well-being.

Data Hiding In Digital Images Using A Partial Optimization Technique Based On The Classical Lsb Method, Feyzi̇ Akar, Yildiray Yalman, Hüseyi̇n Selçuk Varol

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a new partial optimization approach for the least significant bit (LSB) data hiding technique that can be used for protecting any secret information or data. A deterioration effect of as little as possible in an image is intended using the LSB data hiding technique and this is well realized utilizing the proposed partial optimization approach achieving the same data embedding bit rates. In the proposed approach, all of the image pixels are classified into 8 regions and then the 8 distinct ordering codings are applied to each region by the developed partial optimization encoder. Thus, the most …

Optimized Operation And Maintenance Costs To Improve System Reliability By Decreasing The Failure Rate Of Distribution Lines, Hamed Hashemi Dezaki, Seyed Hossein Hosseinian, Hossein Askarian Abyaneh, Seyed Mohammad Mousavi Agah

Turkish Journal of Electrical Engineering and Computer Sciences

Improving distribution system reliability has received a great deal of attention in recent years. Because of the limitation in expected budgets, it is desirable to determine the most efficient strategy to improve system reliability. This paper proposes a novel method to determine the optimized operation and maintenance costs in order to decrease the failure of system components. The proposed objective function includes the average system frequency interruption index (ASIFI) value. To achieve the best strategy to decrease failures of system components, it is necessary to find the minimum value of the objective function, considering the constraints of operation and maintenance …

Optimal Placement And Sizing Of Distributed Generations In Distribution Systems For Minimizing Losses And Thd_V Using Evolutionary Programming, Aida Fazliana Abdul Kadir, Azah Mohamed, Hussain Shareef, Mohd Zamri Che Wanik

Turkish Journal of Electrical Engineering and Computer Sciences

Growing concerns over environmental impacts, improvement of the overall network conditions, and rebate programs offered by governments have led to an increase in the number of distributed generation (DG) units in commercial and domestic electric power production. However, a large number of DG units in a distribution system may sometimes contribute to high levels of harmonic distortion, even though the emission levels of the individual DG units comply with the harmonic standards. It is known that the nonoptimal size and nonoptimal placement of DG units may lead to high power losses, bad voltage profiles, and harmonic propagations. Therefore, this paper …